Input data and models

Each chlorophyll-a (chl-a) model uses remote-sensing reflectances (Rrs) as input. The wavebands used in the calculation are dependent on sensor and model. The Satellite Ocean colour and Phytoplankton Ecology group (SOPhyE) at the Bedford Institute of Oceanography uses daily 4km-resolution satellite data from NASA OBPG to calculate chl-a using different models for use in ocean observation and analysis. NASA OBPG reprocesses their datasets every few years as models improve, after which SOPhyE downloads the new datasets and re-optimizes the coefficients used in certain models. Information on reprocessing versions can be found here.

OCI is the standard chlor_a product distributed in files from NASA OBPG, which uses the empirical band ratio model OCx (O’Reilly et al 1998 in combination with a blend of the Hu CI algorithm (Hu et al 2012) for concentrations <= 0.35 mg m3. For the sensors of interest, the OCI product is also referred to by the following combination of acronyms:

POLY4 is a regionally-tuned version of OCx (Clay et al 2019).

GSM_GS is a regionally-tuned version of the semi-analytical GSM model from Maritorena et al (2002). GS refers to the fact that the g coefficients from the original model are spectrally-dependent in this modification (Clay et al 2019).

EOF is a model that employs Principal Component Analysis, currently in use in the Gulf of Saint Lawrence (Laliberte et al 2018).

In situ samples used for validation:
POLY4 and GSMGS models are both trained using in situ HPLC (High Performance Liquid Chromotography) data. The training set created to calculate EOF chl-a is composed of satellite matchups to in situ chl-a derived from Turner fluorescence, as HPLC data is not available for samples collected in the Gulf of Saint Lawrence.

The oceancolouR package contains the functions ocx(), gsm(), and eof_chl() to implement the chl-a models evaluated here. For eof_chl(), a training set is required for the region of interest. Using the R2022.0-reprocessed data, the re-optimized POLY4 and GSMGS are referred to as poly4v2 and gsmgsv2 - e.g. to use the POLY4 coefficients optimized for R2018.0-reprocessed data, you would use get_ocx_coefs(sensor, region, alg="poly4"), replacing sensor with one of modisaqua, seawifs, or viirssnpp, and region with nwa or nep. To use POLY4 with R2022.0-reprocessed data, you would retrieve the coefficients with get_ocx_coefs(sensor, region, alg="poly4v2").

Region of interest

Bounding boxes for each of the regions of interest are defined as follows:


Matchup restrictions

In situ / satellite matchups used for model training must adhere to the following criteria:

Below is a quick comparison of MODIS-Aqua POLY4_v2 satellite chl-a against in situ HPLC chl-a using different restrictions on the difference in time allowed between the in situ sample and satellite pass for a matchup to be used in training and evaluation:

  1. Sample and pass must be within 12 hours and on the same calendar day
  2. Sample and pass must be within 12 hours
  3. Sample and pass must be within 24 hours and on the same calendar day
  4. Sample and pass must be within 24 hours

Using in situ sample/satellite matchups that are within 24 hours of each other on the same calendar day appears to yield the best results, so that is the restriction used in model training.

Disclaimer: The evaluation metrics of the R2018.0 reprocessing here might have slight differences from those presented in Clay et al 2019 due to changes in exact matchup criteria and the order in which the matchups are filtered. The overall message is the same, however, when possible, the latest reprocessing (R2022.0 as of February 2023) should be used.



Chl-a model comparison with the R2018.0-reprocessed files


Northwest Atlantic

Satellite - in situ matchups collected from 1999-2014.


MODIS-Aqua

SeaWiFS

VIIRS-SNPP


Northeast Pacific

Satellite - in situ matchups collected from 2006-2016.


MODIS-Aqua

SeaWiFS

VIIRS-SNPP


Gulf of Saint Lawrence

Satellite - in situ matchups collected from 1997-2019.


MODIS-Aqua

SeaWiFS

VIIRS-SNPP



Chl-a model comparison with the R2022.0-reprocessed files


Northwest Atlantic

Satellite - in situ matchups collected from 2002-2021


MODIS-Aqua

SeaWiFS

VIIRS-SNPP

OLCI


Gulf of Saint Lawrence

Satellite - in situ matchups collected from 2002-2021.


MODIS-Aqua

SeaWiFS

VIIRS-SNPP

OLCI


Summary

Summary of statistics for each model are listed in the table below. For the NWA, POLY4 is used operationally as it has the best performance in each of the four sensors currently in use. For the GoSL, EOF outperforms the other models. In the NEP for the R2018.0 reprocessing, POLY4 works best with MODIS-Aqua and VIIRS-SNPP, and GSMGS works best with SeaWiFS. Whenever possible, it’s advised to use data from the latest reprocessing, as the models have been improved.

Reprocessing Region Sensor Model Intercept Slope R2 Num. obs. RMSLE
R2018.0 Gulf of Saint Lawrence MODIS-Aqua OCI 0.1334 1.2088 0.1927 2816 0.4779
POLY4 0.2860 1.1972 0.2612 2816 0.5158
GSMGS 0.4920 0.9891 0.3706 1163 0.5930
EOF 0.0077 0.8045 0.4107 2709 0.3030
SeaWiFS OCI 0.1731 0.8625 0.3828 1294 0.4352
POLY4 0.2866 1.0757 0.4110 1294 0.5199
GSMGS 0.6590 1.1115 0.5145 619 0.7746
EOF -0.1033 0.8550 0.3920 1221 0.4138
VIIRS-SNPP OCI -0.0111 1.1959 0.1384 1945 0.4574
POLY4 0.2122 1.2255 0.2227 1945 0.4809
GSMGS 0.3108 0.2546 0.2706 75 0.3371
EOF 0.0112 0.7411 0.3109 1808 0.3095
Northeast Pacific MODIS-Aqua OCI 0.0215 0.9655 0.5946 461 0.3678
POLY4 0.0000 1.0000 0.6666 461 0.3342
GSMGS 0.0356 1.1767 0.6196 387 0.3949
SeaWiFS OCI -0.0421 0.8508 0.6283 40 0.3017
POLY4 0.0000 1.0000 0.7507 40 0.2515
GSMGS -0.0387 0.9041 0.7658 38 0.2375
VIIRS-SNPP OCI -0.0417 0.9296 0.6273 332 0.3411
POLY4 0.0000 1.0000 0.6891 332 0.3150
GSMGS -0.0085 1.1313 0.5812 289 0.3965
Northwest Atlantic MODIS-Aqua OCI -0.0672 0.8386 0.4488 508 0.3714
POLY4 0.0000 1.0000 0.5740 508 0.3341
GSMGS -0.0150 1.0172 0.5050 469 0.3672
SeaWiFS OCI -0.0351 0.6737 0.5544 336 0.3201
POLY4 0.0000 1.0000 0.6216 336 0.3086
GSMGS 0.0109 0.9274 0.5804 304 0.3166
VIIRS-SNPP OCI -0.1175 0.7281 0.3790 172 0.3725
POLY4 0.0000 1.0000 0.5514 172 0.3279
GSMGS 0.0069 1.2068 0.3992 161 0.4475
R2022.0 Gulf of Saint Lawrence MODIS-Aqua OCI 0.2647 1.1857 0.2619 2831 0.4828
POLY4 0.3757 1.2647 0.3114 2831 0.5561
GSMGS 0.4569 0.9534 0.3523 1802 0.5528
EOF 0.1527 1.0849 0.5296 2256 0.3186
OLCI-S3A and OLCI-S3B OCI 0.2365 1.4162 0.2577 430 0.4355
POLY4 0.1892 1.3112 0.2802 430 0.3789
GSMGS 0.5346 1.0984 0.4190 138 0.5795
EOF 0.0256 1.1500 0.4037 303 0.2433
SeaWiFS OCI 0.2577 1.1426 0.3028 1433 0.4689
POLY4 0.4037 1.1349 0.3490 1433 0.5529
GSMGS 0.6902 1.2501 0.4072 931 0.8028
EOF 0.1391 0.9913 0.4523 1030 0.3140
VIIRS-SNPP OCI 0.2054 1.2457 0.2364 1833 0.4711
POLY4 0.4173 1.1754 0.3129 1833 0.5670
GSMGS 0.4673 0.3269 0.0452 30 0.4290
EOF 0.2169 0.9543 0.4968 1478 0.3400
Northwest Atlantic MODIS-Aqua OCI -0.0666 0.6458 0.4402 789 0.3873
POLY4 0.0000 1.0000 0.5672 789 0.3590
GSMGS 0.0029 0.9807 0.5129 758 0.3835
OLCI-S3A and OLCI-S3B OCI 0.1016 0.7065 0.5569 120 0.3372
POLY4 0.0000 1.0001 0.6404 120 0.2452
GSMGS -0.0014 0.9998 0.4715 112 0.3110
SeaWiFS OCI -0.0266 0.7129 0.6374 121 0.3502
POLY4 0.0000 1.0000 0.6803 121 0.3255
GSMGS 0.0000 1.0000 0.6239 111 0.3647
VIIRS-SNPP OCI -0.0491 0.6530 0.4377 562 0.3658
POLY4 0.0000 1.0000 0.5911 562 0.3314
GSMGS 0.0000 1.0000 0.5572 523 0.3486


References

Clay, S.; Pena, A.; DeTracey, B.; Devred, E. Evaluation of Satellite-Based Algorithms to Retrieve Chlorophyll-a Concentration in the Canadian Atlantic and Pacific Oceans. Remote Sens. 2019, 11, 2609.

Hu, Chuanmin & Lee, Zhongping & Franz, Bryan. (2012). Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference. Journal of Geophysical Research. 117. C01011. 10.1029/2011JC007395.

Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., & Proctor, C. W. (2019). Improving satellite global chlorophyll a data products through algorithm refinement and data recovery. Journal of Geophysical Research: Oceans, 124, 1524– 1543. https://doi.org/10.1029/2019JC014941

Laliberté, Julien & Larouche, Pierre & Devred, Emmanuel & Craig, Susanne. (2018). Chlorophyll-a Concentration Retrieval in the Optically Complex Waters of the St. Lawrence Estuary and Gulf Using Principal Component Analysis. Remote Sensing. 10. 10.3390/rs10020265.

Maritorena, Stephane & Siegel, David & Peterson, Alan. (2002). Optimization of a semianalytical ocean color model for global-scale application. Applied optics. 41. 2705-14. 10.1364/AO.41.002705.

O’Reilly, John & Maritorena, S. & Mitchell, B.G. & Siegel, David & Carder, Kendall & Garver, S.A. & Kahru, Mati & Mcclain, Charles. (1998). Ocean color chlorophyll algorithms for SeaWiFS. Journal of Geophysical Research. 103. 937-953. 10.1029/98JC02160.